


import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[int]									=  F'{sampling_rate}'
							__snake_case						:     List[Any]									=  "1"
							__snake_case						:     List[Any]									=  "f32le"
							__snake_case						:     Any									=  [
							    "ffmpeg",
							    "-i",
							    "pipe:0",
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]

							try:
														with subprocess.Popen(__lowerCamelCase				,      stdin=subprocess.PIPE				,      stdout=subprocess.PIPE      ) as ffmpeg_process:
																					__snake_case						:     Any									=  ffmpeg_process.communicate(__lowerCamelCase      )
							except FileNotFoundError as error:
														raise ValueError("ffmpeg was not found but is required to load audio files from filename"      ) from error
							__snake_case						:     str									=  output_stream[0]
							__snake_case						:     List[str]									=  np.frombuffer(__lowerCamelCase				,      np.floataa      )
							if audio.shape[0] == 0:
														raise ValueError("Malformed soundfile"      )
							return audio







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = "f32le"				,      ):
							__snake_case						:     List[Any]									=  F'{sampling_rate}'
							__snake_case						:     List[str]									=  "1"
							if format_for_conversion == "s16le":
														__snake_case						:     Optional[int]									=  2
							elif format_for_conversion == "f32le":
														__snake_case						:     str									=  4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'      )

							__snake_case						:     List[str]									=  platform.system()
							if system == "Linux":
														__snake_case						:     Tuple									=  "alsa"
														__snake_case						:     Union[str, Any]									=  "default"
							elif system == "Darwin":
														__snake_case						:     str									=  "avfoundation"
														__snake_case						:     Any									=  ":0"
							elif system == "Windows":
														__snake_case						:     Any									=  "dshow"
														__snake_case						:     Optional[int]									=  "default"

							__snake_case						:     List[str]									=  [
							    "ffmpeg",
							    "-f",
							    format_,
							    "-i",
							    input_,
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-fflags",
							    "nobuffer",
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]
							__snake_case						:     str									=  int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
							__snake_case						:     Tuple									=  _ffmpeg_stream(__lowerCamelCase				,      __lowerCamelCase      )
							for item in iterator:
														yield item







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = None				,      __lowerCamelCase = None				,      __lowerCamelCase = "f32le"				,      ):
							if stream_chunk_s is not None:
														__snake_case						:     str									=  stream_chunk_s
							else:
														__snake_case						:     Union[str, Any]									=  chunk_length_s

							__snake_case						:     Union[str, Any]									=  ffmpeg_microphone(__lowerCamelCase				,      __lowerCamelCase				,      format_for_conversion=__lowerCamelCase      )
							if format_for_conversion == "s16le":
														__snake_case						:     List[Any]									=  np.intaa
														__snake_case						:     int									=  2
							elif format_for_conversion == "f32le":
														__snake_case						:     List[Any]									=  np.floataa
														__snake_case						:     Optional[int]									=  4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'      )

							if stride_length_s is None:
														__snake_case						:     Optional[Any]									=  chunk_length_s / 6
							__snake_case						:     Union[str, Any]									=  int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
							if isinstance(__lowerCamelCase				,      (int, float)      ):
														__snake_case						:     Tuple									=  [stride_length_s, stride_length_s]

							__snake_case						:     List[Any]									=  int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
							__snake_case						:     Any									=  int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
							__snake_case						:     Tuple									=  datetime.datetime.now()
							__snake_case						:     Union[str, Any]									=  datetime.timedelta(seconds=__lowerCamelCase      )
							for item in chunk_bytes_iter(__lowerCamelCase				,      __lowerCamelCase				,      stride=(stride_left, stride_right)				,      stream=__lowerCamelCase      ):
														# Put everything back in numpy scale
														__snake_case						:     Union[str, Any]									=  np.frombuffer(item["raw"]				,      dtype=__lowerCamelCase      )
														__snake_case						:     List[str]									=  (
														    item["stride"][0] // size_of_sample,
														    item["stride"][1] // size_of_sample,
														)
														__snake_case						:     Optional[Any]									=  sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 1_0 * delta:
																					# We're late !! SKIP
																					continue
														yield item







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False      ):
							__snake_case						:     Optional[Any]									=  b""
							__snake_case					,     __snake_case						:     str									=  stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'      )
							__snake_case						:     Optional[int]									=  0
							for raw in iterator:
														acc += raw
														if stream and len(__lowerCamelCase      ) < chunk_len:
																					__snake_case						:     Tuple									=  (_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(__lowerCamelCase      ) >= chunk_len:
																												# We are flushing the accumulator
																												__snake_case						:     List[str]									=  (_stride_left, stride_right)
																												__snake_case						:     int									=  {"raw": acc[:chunk_len], "stride": stride}
																												if stream:
																																			__snake_case						:     Any									=  False
																												yield item
																												__snake_case						:     Optional[int]									=  stride_left
																												__snake_case						:     Dict									=  acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(__lowerCamelCase      ) > stride_left:
														__snake_case						:     int									=  {"raw": acc, "stride": (_stride_left, 0)}
														if stream:
																					__snake_case						:     Optional[Any]									=  False
														yield item







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[int]									=  2**2_4  # 16Mo
							try:
														with subprocess.Popen(__lowerCamelCase				,      stdout=subprocess.PIPE				,      bufsize=__lowerCamelCase      ) as ffmpeg_process:
																					while True:
																												__snake_case						:     Any									=  ffmpeg_process.stdout.read(__lowerCamelCase      )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError("ffmpeg was not found but is required to stream audio files from filename"      ) from error






'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




